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28th Euromicro International Conference on parallel, distributed and Network-Based processing (PDP)

作者： Ramaswamy, Arunselvan Paderborn Univ Dept Elect & Comp Sci Engn D-33098 Paderborn Germany

ISBN: (纸本)9781728165820

distributed descent-based methods are an essential toolset to solving optimization problems in multi-agent system scenarios. Here the agents seek to optimize a global objective function through mutual cooperation. Oftentimes, cooperation is achieved over a wireless communication network that is prone to delays and errors. There are many scenarios wherein the objective function is either non-differentiable or merely observable. In this paper, we present a cross-entropy based distributed stochastic approximation algorithm (SA) that finds a minimum of the objective, using only samples. We call this algorithm Decentralized Simultaneous Perturbation Stochastic Gradient, with Constant Sensitivity Parameters (DSPG). This algorithm is a two fold improvement over the classic Simultaneous Perturbation Stochastic Approximations (SPSA) algorithm. Specifically, DSPG allows for (i) the use of old information from other agents and (ii) easy implementation through the use simple hyper-parameter choices. We analyze the biases and variances that arise due to these two allowances. We show that the biases due to communication delays can be countered by a careful choice of algorithm hyper-parameters. The variance of the gradient estimator and its effect on the rate of convergence is studied. We present numerical results supporting our theory. Finally, we discuss an application to the stochastic consensus problem.

关键词： distributed optimization distributed approximate gradient method cross-entropy based gradient approximation optimization over networks with unbounded delays

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基于分布式执行框架的低频射电干涉阵列成像管线优化

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天文学报 2023年第6期64卷 99-112页

作者：韦耀杰符杰林劳保强桂林电子科技大学信息与通信学院认知无线电与信息处理教育部重点实验室桂林541004 中国科学院上海天文台上海200030 云南大学物理与天文学院昆明650500

平方公里阵列(Square Kilometre Array,SKA)项目是建设全球最大射电望远镜的国际合作项目,其灵敏度和测量速度将比当前所有的射电望远镜都要高出一个数量级.连续谱巡天是SKA的主要观测模式之一,基于连续谱成像建立巡天区域的标准星图,... 详细信息

平方公里阵列(Square Kilometre Array,SKA)项目是建设全球最大射电望远镜的国际合作项目,其灵敏度和测量速度将比当前所有的射电望远镜都要高出一个数量级.连续谱巡天是SKA的主要观测模式之一,基于连续谱成像建立巡天区域的标准星图,将能为后续天文科学研究奠定重要基础.银河系与河外星系全天默奇森宽场阵列拓展巡天(GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended,GLEAM-X)是2018--2020年利用SKA先导望远镜默奇森宽场阵列(Murchison Wide-field Array,MWA)二期拓展阵列开展的新的射电连续谱巡天项目,观测期间积累了大量的低频巡天观测数据.海量观测数据的自动化、大批量处理是SKA望远镜项目所面临的的最大挑战和难题之一,基于分布式执行框架的成像管线优化经验将有助于解决海量数据处理问题.详细介绍了GLEAM-X成像管线并对其进行整合和改进,在中国SKA区域中心原型机(China SKA Regional Centre Prototype,CSRC-P)上实现了多条管线并行处理,使用GLEAM-X观测数据验证成像管线系统的部署和测试其正确性.随后为了优化管线提高处理效率,使用数据激活流图形引擎(Data Activated Liu Graph Engine,DALiuGE),将成像管线集成入DALiuGE执行框架中实现了管线的自动化分布式并行处理.通过性能测试与结果分析表明,基于DALiuGE执行框架进行优化的成像管线相较于传统的并行方式具有更好的性能优势、更灵活的适配性和可扩展性,可支持未来SKA第一阶段试运行期间的大规模连续谱成像实验.

关键词：仪器:干涉仪方法:观测技术:干涉测量技术:图像处理射电连续谱:普通

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OpenMedIA: Open-Source Medical image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms

arXiv

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arXiv 2022年

作者： Zhuang, Jia-Xin Huang, Xiansong Yang, Yang Chen, Jiancong Yu, Yue Gao, Wei Li, Ge Chen, Jie Zhang, Tong Peng Cheng Laboratory Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Electronic and Computer Engineering Peking University China National Laboratory for Parallel and Distributed Processing National University of Defense Technology China

In this paper, we present OpenMedIA, an open-source toolbox library containing a rich set of deep learning methods for medical image analysis under heterogeneous Artificial Intelligence (AI) computing platforms. Various medical image analysis methods, including 2D/3D medical image classification, segmentation, localisation, and detection, have been included in the toolbox with PyTorch and/or MindSpore implementations under heterogeneous NVIDIA and Huawei Ascend computing systems. To our best knowledge, OpenMedIA is the first open-source algorithm library providing compared PyTorch and MindSpore implementations and results on several benchmark datasets. The source codes and models are available at https://***/ OpenMedIA. © 2022, CC BY-NC-SA.

关键词： Open systems

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Classification Of Human Facial Expressions For Emotion Recognition Using A distributed Computer System 5

Classification Of Human Facial Expressions For Emotion Recog...

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5th International Conference on Computer Science and Engineering (UBMK)

作者： Korkmaz, Timucin Erol, Hamza Mersin Univ Grad Sch Nat & Appl Sci Dept Comp Engn Mersin Turkey Mersin Univ Engn Fac Dept Comp Engn Mersin Turkey

ISBN: (纸本)9781728175652

A computer system was developed to classify human facial expressions for emotion recognition using a distributed computer architecture within the scope of big data. Computers with normal standards and features were used in the distributed computer architecture. Necessary system software for the distributed computer architecture was established and mutual communication protocols were provided between the computers and databases in the computer network. Visual C# parallel programming was used as the software language on the distributed computer architecture. In the software prepared, face image files were processed and threads were created. The threads created later were processed in the processors of the computers. The threads were run on the processors in the distributed computer system, and facial expressions were classified for emotion recognition. In the distributed computer architecture: the number of image files, the volume of big data and the load of the threads to be processed arc taken into account;and databases and parallel programming were used and the classification of human face images was performed. Emotion analysis methods and facial expression recognition techniques were used in the classification process. The distributed computer system is in low cost and high processing speed. With the distributed computer system architecture, big data analytics has become convenient and feasible.

关键词： Big data distributed System Emotion analysis image classification parallel Programing Face expression recognition

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yan Jia Yuqing Cheng Kele Xu Yong Dou Peng Qiao Zhouyu He National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue’s surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network’s ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI.

关键词： Training Ultrasonic imaging Tongue Self-supervised learning Data augmentation Data models Cognition Data mining Noise measurement Signal to noise ratio

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PickDet: A Detection Framework for Aerial-view Scene

PickDet: A Detection Framework for Aerial-view Scene

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IEEE Visual Communications and image processing (VCIP)

作者： Cheng Lyu Xiao Deng Shizun Wang Ming Wu Chuang Zhang Pattern Recognition and Intelligent Systems Lab. Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665475938

Detecting objects in the aerial-view scene is challenging for the objects usually have small scales relative to the image, making it hard to achieve high accuracy in full-image detection. Slice detection tries to overcome this by cutting the full image into slices before detecting them, but objects are sparsely distributed and usually clustered in local areas, a large number of background areas without objects can be ignored to improve detection efficiency. In this paper, we present PickDet, a framework for efficient and effective object detection in the aerial-view scene, which only chooses slices containing objects to conduct detection. The key components of PickDet include a lightweight convolutional network (PickNet), a screening strategy (SoftPick), and fine-tuned detectors. Given slices of aerial-view images, PickNet first outputs the probability of object existence. Then SoftPick conducts a double-threshold screening strategy to pick the slices which contain objects. Finally, all picked slices are fed into the detector in parallel and full-image detection is used as an auxiliary mean. Compared with previous methods, PickDet achieves higher accuracy and more efficiency in the aerial-view scene. We evaluate PickDet on Visdrone and Oiltank datasets, experiments show that PickDet can result in up to 28.0% AP improvement compared to full-image detection, and can result in up to 2.9% AP increase and up to 5 times inference speedup compared to slice detection.

关键词： Fault tolerance Visual communication image processing Fault tolerant systems Detectors Object detection

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An Effective Automatic Recognition Approach of Chinese Herbal Medicine by Micro image processing

An Effective Automatic Recognition Approach of Chinese Herba...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Guangyao Pang Zhenming Yu Honglin Pan Chengwen Xie Ping Gong Xiaoying Zhu Zizhen Peng Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China

The identification of traditional Chinese medicine is the key to control the quality of traditional Chinese medicine and ensure the safety and effectiveness of clinical medication. Compared with the physical and chemical identification methods with expensive equipment and complex operation, microscopic image identification of traditional Chinese medicine is an effective method with low cost. However, this method still has a high learning cost and identification errors due to staff fatigue. Therefore, this paper designs an effective automatic recognition approach of Chinese herbal medicine by micro image processing. The core of this method is the introduction of transfer learning and data enhancement methods, which effectively alleviates the problem of insufficient number of microscopic image data samples in the microscopic recognition of traditional Chinese medicine, and realizes the automatic recognition of traditional Chinese medicine. We construct a library of microscopic recognition features of Chinese herbal medicine, and designe evaluation experiments on this basis. The results show that the recognition performance of our method is better than that of SSD method, especially the F1 value is increased by 7.25 %.

关键词： Deep learning image recognition Costs Microscopy Transfer learning Ubiquitous computing Raw materials

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HCGrid: a convolution-based gridding framework for radio astronomy in hybrid computing environments

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MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 2021年第2期501卷 2734-2744页

作者： Wang, Hao Yu, Ce Zhang, Bo Xiao, Jian Luo, Qi Tianjin Univ Coll Intelligence & Comp 135 Yaguan RoodHaihe Educ Pk Tianjin 300350 Peoples R China Chinese Acad Sci Natl Astron Observ 20 Datun Rd Beijing 100012 Peoples R China Chinese Acad Sci Natl Astron Observ CAS Key Lab FAST 20 Datun Rd Beijing 100012 Peoples R China

Gridding operation, which is to map non-uniform data samples on to a uniformly distributed grid, is one of the key steps in radio astronomical data reduction process. One of the main bottlenecks of gridding is the poor computing performance, and a typical solution for such performance issue is the implementation of multicore CPU platforms. Although such a method could usually achieve good results, in many cases, the performance of gridding is still restricted to an extent due to the limitations of CPU, since the main workload of gridding is a combination of a large number of single instruction, multidata stream operations, which is more suitable for GPU, rather than CPU implementations. To meet the challenge of massive data gridding for the modern large single-dish radio telescopes, e.g. the Five-hundred-meter Aperture Spherical radio Telescope, inspired by existing multicore CPU gridding algorithms such as Cygrid, here we present an easy-to-install, high-performance, and open-source convolutional gridding framework, HCGrid, in CPU-GPU heterogeneous platforms. It optimizes data search by employing multithreading on CPU, and accelerates the convolution process by utilizing massive parallelization of GPU. In order to make HCGrid a more adaptive solution, we also propose the strategies of thread organization and coarsening, as well as optimal parameter settings under various GPU architectures. A thorough analysis of computing time and performance gain with several GPU parallel optimization strategies show that it can lead to excellent performance in hybrid computing environments.

关键词： methods: data analysis techniques: image processing software: public release

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A comparison study on nonlinear dimension reduction methods with kernel variations: Visualization, optimization and classification

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INTELLIGENT DATA ANALYSIS 2020年第2期24卷 267-290页

作者： Kempfert, Katherine C. Wang, Yishi Chen, Cuixian Wong, Samuel W. K. Univ Florida Gainesville FL USA Univ North Carolina Wilmington Wilmington NC 28403 USA Univ Waterloo Waterloo ON Canada

Because of high dimensionality, correlation among covariates, and noise contained in data, dimension reduction (DR) techniques are often employed to the application of machine learning algorithms. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and their kernel variants (KPCA, KLDA) are among the most popular DR methods. Recently, Supervised Kernel Principal Component Analysis (SKPCA) has been shown as another successful alternative. In this paper, brief reviews of these popular techniques are presented first. We then conduct a comparative performance study based on three simulated datasets, after which the performance of the techniques are evaluated through application to a pattern recognition problem in face image analysis. The gender classification problem is considered on MORPH-ii and FG-NET, two popular longitudinal face aging databases. Several feature extraction methods are used, including biologically-inspired features (BIF), local binary patterns (LBP), histogram of oriented gradients (HOG), and the Active Appearance Model (AAM). After applications of DR methods, a linear support vector machine (SVM) is deployed with gender classification accuracy rates exceeding 95% on MORPH-ii, competitive with benchmark results. A parallel computational approach is also proposed, attaining faster processing speeds and similar recognition rates on MORPH-ii. Our computational approach can be applied to practical gender classification systems and generalized to other face analysis tasks, such as race classification and age prediction.

关键词： Dimension reduction PCA LDA FDA KPCA KFDA SKPCA SVM parameter optimization gender classification MORPH-ii

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Displacement detection method of QR code reference object based on computer vision 18

Displacement detection method of QR code reference object ba...

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18th IEEE Int Symp on parallel and distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Yang, Qiuhui Ji, Xiaoyu Liang, Yanyan Wuzhou Univ Guangxi Coll & Univ Key Lab Image Proc & Intellig Wuzhou Peoples R China Wuzhou Univ Sch Data Sci & Software Engn Wuzhuo Peoples R China Macau Univ Sci & Technol Fac Informat Technol Macau Peoples R China Beijing Inst Technol Zhuhai Peoples R China

ISBN: (纸本)9781665414852

In order to realize the concrete quality monitoring, the displacement detection method based on computer vision is adopted by adding the identification label to the concrete specimen The traditional camera calibration methods often need to make standard checkerboard to calculate the camera parameters. If users use different mobile phones to shoot the same scene, it is impossible to calibrate each mobile camera in advance. Therefore, aiming at the problem that the traditional camera calibration method can't meet the needs of some application scenarios, this paper proposes a method to calibrate the camera by extracting a small number of feature points in the image. Experimental data show that the method can calibrate the camera accurately, and then calculate the reference moving distance according to the basic principle of computer vision, and it has good flexibility.

关键词： Displacement detection camera calibration feature point identification label

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